Cheminformatics on the Web (2021)
Here, I have compiled a list of some tools and possible solutions. The web is a nice platform, it is available anywhere and just requires an internet connection. I, personally like static websites which don't require a server side application and can be hosted on platforms like GitHub Pages. Or, just open the HTML file and run it in your browser. No data is required to be sent to any server and your device's computational power is used. Even our phones have a lot of computational power now, which allows the user to run tasks on the go without needing to worry about managing dependencies. WebAssembly (Wasm) has made running code written for other platfroms on the web relativevly easier. Combine Wasm with some pure JavaScript libraries, and you get a platform to quickly amp up your speed in some common tasks.
RDKit
RDKit bundles a minimal JavaScript Wrapper in their core RDKit suite. This is perfect for generating 2D Figures (HTML5 Canva/SVGs), Cannonical SMILES, Descriptors e.t.c
Substructure Matching
This can be used to flag undesirable functional groups in a given compound. Create a simple key:value pair of name:SMARTS and use it to highlight substructure matches. Thus, something like PostEra's Medicinal Chemistry Alert can be done with RDKit-JS alone.
Computing Properties
This is useful to calculate basic properties of a given compound.
Webina - Molecular Docking
Webina is a JavaScript/Wasm library that runs AutoDock Vina, which can enable you to run Molecular Docking straight in the browser itself.
Obviously, it takes a few hits in the time to complete the docking because the code is transpiled from C++ to Wasm. But, the only major drawback (for now) is that it uses SharedArrayBuffer. Due to Spectre, this feature was disabled on all browsers. Currently, only Chromium-based and Firefox browsers have reimplemented and renabled it. Hopefully, soon this will be again supported by all major browsers.
Machine Learning
Frameworks have now evolved enough to allow exporting models to be able to run them through JavaScript/Wasm backend. An example task can be NER or Named-entity Recognition. It can be used to extract compounds or diseases from a large blob of text and then matched with external refferences. Another example is target-prediction right in the browser: CHEMBL - Target Prediction in Browser
CHEMBL Group is first training the model using PyTorch (A Python ML Library), then converting it to the ONNX runtime. A model like this can be directly implemented in Tensorflow, and then exported to be able to run with TensorFlow.js
Cheminfo-to-web
The project aims to port chemoinformatics libraries into JavaScript via Emscripten. They have ported InChI, Indigo, OpenBabel, and OpenMD
Kekule.js
It is written by @partridgejiang, who is behind the Cheminfo-to-web project
It is molecule-centric, focusing on providing the ability to represent, draw, edit, compare and search molecule structures on web browsers.
Browser Extensions
The previous machine learning examples can be packaged as browser-extensions to perform tasks on the article you are reading. With iOS 15 bringing WebExtensions to iOS/iPadOS, the same browser extension source code can be now used on Desktop and Mobile Phones. You can quickly create an extenison to convert PDB codes into links to RCSB, highlight SMILES, highlight output of NER models, e.t.c
Conclusion
I have not even touched all the bases of cheminformatics for the web here. There is still a lot more to unpack. Hopefully, this encourages you to explore the world of cheminformatics on the web.